An autonomous driving system requires precise sensor information (such as images captured by a camera installed on a vehicle) to generate reliable control signals and safely perform autonomous driving of the vehicle. When the camera's field-of-view is partially blocked, the captured images may contain a blurred region with little, if any, details. Without sufficient image details showing the surrounding environment, the autonomous driving system may not be able to determine the road and traffic conditions and cannot guarantee the safety of a self-driving vehicle. Therefore, it is crucial to quickly and accurately detect blockages in the camera's field-of-view, so that the autonomous driving system can adjust the controlling of the vehicle or provide a warning of the blockage to a human controller.
Many real-life scenarios may lead to false determinations of a camera's field-of-view being blocked. Existing approaches are generally based on a “single-image blur detection” mechanism, which may not be sufficient to detect those scenarios in which only a small portion of the camera's field-of-view is blocked. Also, existing approaches may have a high false-positive rate when the captured images inaccurately resemble a camera being blocked, given that a single image frame contains limited information. Thus, it is challenging to design a blockage detection system to achieve a high blockage detection rate and a low/zero false-positive rate.